Citation
Algorithm preformance prediction for nurse rostering. In proceedings of the 6th Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2013), 27 - 30 Aug 2013, Ghent, Belgium, pages 21-38, 2013.
Paper
Abstract
In this paper, we investigate accurate performance prediction models for nurse rostering algorithms. The study is based on results of a similar approach for formally stated decision problems. Due to the complex nature of nurse rostering problems, we are bound to useheuristicmethodsthatdelivergoodsolutionsinareasonableamountoftime.Thereisno guaranteeofsolutionsbeingoptimal.Inpractice,itisnotevenpossibletoemployexhaustive searchmethodsonrealisticprobleminstances.Wequantifytheperformanceofanalgorithm as the quality of the obtained solution, after a predetermined amount of computation time. Webuildpredictionmodelsbasedonsimplecharacteristicsoftheinstances.Wedevelop a feature set that characterises the instances in terms of simple values. Such an approach allowsforfastandaccuratepredictions.Theapplicationofthemethodologytonurserostering has not been attempted before.
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Bibtex
@INPROCEEDINGS{2013-021-038-P, author = {T. Messelis and P. De Causmaecker and G.Van den Berghe},
title = {Algorithm preformance prediction for nurse rostering},
booktitle = {In proceedings of the 6th Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2013), 27 - 30 Aug 2013, Ghent, Belgium},
year = {2013},
editor = {G. Kendall and B. McCollum and G. {Venden Berghe}},
pages = {21--38},
note = {Paper},
abstract = { In this paper, we investigate accurate performance prediction models for nurse rostering algorithms. The study is based on results of a similar approach for formally stated decision problems. Due to the complex nature of nurse rostering problems, we are bound to useheuristicmethodsthatdelivergoodsolutionsinareasonableamountoftime.Thereisno guaranteeofsolutionsbeingoptimal.Inpractice,itisnotevenpossibletoemployexhaustive searchmethodsonrealisticprobleminstances.Wequantifytheperformanceofanalgorithm as the quality of the obtained solution, after a predetermined amount of computation time. Webuildpredictionmodelsbasedonsimplecharacteristicsoftheinstances.Wedevelop a feature set that characterises the instances in terms of simple values. Such an approach allowsforfastandaccuratepredictions.Theapplicationofthemethodologytonurserostering has not been attempted before.},
owner = {Graham},
timestamp = {2017.01.16},
webpdf = {2013-021-038-P.pdf} }